This PDF file contains the front matter associated with SPIE Proceedings Volume 10436, including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Landsat data are widely used in various earth observations, but the clouds interfere with the applications of the images. This paper proposes a weighted variational gradient-based fusion method (WVGBF) for high-fidelity thin cloud removal of Landsat images, which is an improvement of the variational gradient-based fusion (VGBF) method. The VGBF method integrates the gradient information from the reference band into visible bands of cloudy image to enable spatial details and remove thin clouds. The VGBF method utilizes the same gradient constraints to the entire image, which causes the color distortion in cloudless areas. In our method, a weight coefficient is introduced into the gradient approximation term to ensure the fidelity of image. The distribution of weight coefficient is related to the cloud thickness map. The map is built on Independence Component Analysis (ICA) by using multi-temporal Landsat images. Quantitatively, we use R value to evaluate the fidelity in the cloudless regions and metric Q to evaluate the clarity in the cloud areas. The experimental results indicate that the proposed method has the better ability to remove thin cloud and achieve high fidelity.

The project of The First National Geographic Conditions Census developed by Chinese government has designed the data acquisition content and indexes, and has built corresponding classification system mainly based on the natural property of material. However, the unified standard for land cover classification system has not been formed; the production always needs converting to meet the actual needs. Therefore, it proposed a refined classification method based on multi source of remote sensing information fusion. It takes the third-level classes of forest land and grassland for example, and has collected the thematic data of Vegetation Map of China (1:1,000,000), attempts to develop refined classification utilizing raster spatial analysis model. Study area is selected, and refined classification is achieved by using the proposed method. The results show that land cover within study area is divided principally among 20 classes, from subtropical broad-leaved forest (31131) to grass-forb community type of low coverage grassland (41192); what's more, after 30 years in the study area, climatic factors, developmental rhythm characteristics and vegetation ecological geographical characteristics have not changed fundamentally, only part of the original vegetation types have changed in spatial distribution range or land cover types. Research shows that refined classification for the third-level classes of forest land and grassland could make the results take on both the natural attributes of the original and plant community ecology characteristics, which could meet the needs of some industry application, and has certain practical significance for promoting the product of The First National Geographic Conditions Census.

Automatic road extraction from High Resolution Remote Sensing Image is a challenging problem. In this paper we present a new approach for road automatically extraction which is based on topological derivative and mathematical morphology. This approach for road extraction can be divided into three main steps: using topological derivative for image segmentation, using mathematical morphology for road network identification and filtering. The experimental results show that this approach can effectively extract roads from high-resolution remote sensing image.

The data of GIS have spatial distribution. Geographic data has both spatial characteristics and attribute characteristics, and also changes with time. Therefore, the amount of data is very large. Nowadays, many industries and departments in the society are using GIS. However, without proper data analysis and mining scheme, GIS will not exert its maximum effectiveness and will waste a lot of data.

In this paper, we use the geographic information demand of a national security department as the experimental object, combining the characteristics of GIS data, taking into account the characteristics of time, space, attributes and so on, and using cluster analysis algorithm. We further study the mining scheme for depth data, and get the algorithm model. This algorithm can automatically classify sample data, and then carry out exploratory analysis. The research shows that the algorithm model and the information mining scheme can quickly find hidden depth information from the surface data of GIS, thus improving the efficiency of the security department. This algorithm can also be extended to other fields.

Aircraft detection from very high resolution remote sensing images, has gained more increasing interest in recent years due to the successful civil and military applications. However, several problems still exist: 1) how to extract the high-level features of aircraft; 2) locating objects within such a large image is difficult and time consuming; 3) A common problem of multiple resolutions of satellite images still exists. In this paper, inspirited by biological visual mechanism, the fusion detection framework is proposed, which fusing the top-down visual mechanism (deep CNN model) and bottom-up visual mechanism (GBVS) to detect aircraft. Besides, we use multi-scale training method for deep CNN model to solve the problem of multiple resolutions. Experimental results demonstrate that our method can achieve a better detection result than the other methods.

This paper proposes an effective tensor-based spatiotemporal saliency computation model for saliency detection in videos. First, we construct the tensor representation of video frames. Then, the spatiotemporal saliency can be directly computed by the tensor distance between different tensors, which can preserve the complete temporal and spatial structure information of object in the spatiotemporal domain. Experimental results demonstrate that our method can achieve encouraging performance in comparison with the state-of-the-art methods.

This article presents an experiment of water depth inversion using the band ratio method in Dongsha Island shallow water. The remote sensing data is from QuickBird satellite on April 19, 2004. The bathymetry result shows an extensive agreement with the charted depths. 129 points from the chart depth data were chosen to evaluate the accuracy of the inversion depth. The results show that when the water depth is less than 20m, the inversion depth is accord with the chart, while the water depth is more than 20m, the inversion depth is still among 15- 25m. Therefore, the remote sensing methods can only be effective with the inversion of 20m in Dongsha Island shallow water, rather than in deep water area. The total of 109 depth points less than 20m were used to evaluate the accuracy, the root mean square error is 2.2m.

The remote sensing image is usually polluted by atmosphere components especially like aerosol particles. For the quantitative remote sensing applications, the radiative transfer model based atmospheric correction is used to get the reflectance with decoupling the atmosphere and surface by consuming a long computational time. The parallel computing is a solution method for the temporal acceleration. The parallel strategy which uses multi-CPU to work simultaneously is designed to do atmospheric correction for a multispectral remote sensing image. The parallel framework’s flow and the main parallel body of atmospheric correction are described. Then, the multispectral remote sensing image of the Chinese Gaofen-2 satellite is used to test the acceleration efficiency. When the CPU number is increasing from 1 to 8, the computational speed is also increasing. The biggest acceleration rate is 6.5. Under the 8 CPU working mode, the whole image atmospheric correction costs 4 minutes.

Conventional singe baseline InSAR is easily affected by atmospheric artifacts, making it difficult to generate highprecision DEM. To solve this problem, in this paper, a multi-baseline interferometric phase accumulation method with weights fixed by coherence is proposed to generate higher accuracy DEM. The mountainous area in Kunming, Yunnan Province, China is selected as study area, which is characterized by cloudy weather, rugged terrain and dense vegetation. The multi-baseline InSAR experiments are carried out by use of four ALOS-2 PALSAR-2 images. The generated DEM is evaluated by Chinese Digital Products of Fundamental Geographic Information 1:50000 DEM. The results demonstrate that: 1) the proposed method can reduce atmospheric artifacts significantly; 2) the accuracy of InSAR DEM generated by six interferograms satisfies the standard of 1:50000 DEM Level Three and American DTED-1.

Large-scale oceansat remote sensing images cover a big area sea surface, which fluctuation can be considered as a non-stationary process. Short-Time Fourier Transform (STFT) is a suitable analysis tool for the time varying nonstationary signal. In this paper, a novel ship detection method using 2-D STFT sea background statistical modeling for large-scale oceansat remote sensing images is proposed. First, the paper divides the large-scale oceansat remote sensing image into small sub-blocks, and 2-D STFT is applied to each sub-block individually. Second, the 2-D STFT spectrum of sub-blocks is studied and the obvious different characteristic between sea background and non-sea background is found. Finally, the statistical model for all valid frequency points in the STFT spectrum of sea background is given, and the ship detection method based on the 2-D STFT spectrum modeling is proposed. The experimental result shows that the proposed algorithm can detect ship targets with high recall rate and low missing rate.

Multiscale segmentation of images can effectively form boundaries of different objects with different scales. However, for the remote sensing image which widely coverage with complicated ground objects, the number of suitable segmentation scales, and each of the scale size is still difficult to be accurately determined, which severely restricts the rapid information extraction of the remote sensing image. A great deal of experiments showed that the normalized difference vegetation index (NDVI) can effectively express the spectral characteristics of a variety of ground objects in remote sensing images. This paper presents a method using NDVI assisted adaptive segmentation of remote sensing images, which segment the local area by using NDVI similarity threshold to iteratively select segmentation scales. According to the different regions which consist of different targets, different segmentation scale boundaries could be created. The experimental results showed that the adaptive segmentation method based on NDVI can effectively create the objects boundaries for different ground objects of remote sensing images.

Polarimetric SAR (PolSAR) image classification is one of the important applications of PolSAR remote sensing. It is a difficult high-dimension nonlinear mapping problem, the sparse representations based on learning overcomplete dictionary have shown great potential to solve such problem. The overcomplete dictionary plays an important role in PolSAR image classification, however for PolSAR image complex scenes, features shared by different classes will weaken the discrimination of learned dictionary, so as to degrade classification performance. In this paper, we propose a novel overcomplete dictionary learning model to enhance the discrimination of dictionary. The learned overcomplete dictionary by the proposed model is more discriminative and very suitable for PolSAR classification.

Grassland fire has the characteristics of fierce fire and rapid spreading, and many fires occur in sparsely populated places. Satellite remote sensing has the characteristics of fast imaging period and wide coverage, and plays an important role in the rapid monitoring and evaluation of grassland fire. FY-3 satellite has been widely used since its launch in September 2008, and this paper uses the fire information of Gansu grassland from 2011 to 2016, based on the more mature MODIS and NOAA-AVHRR fire identification method. The results show that the accuracy of FY-3/VIRR satellite data fire detection are higher than that of NOAA-AVHRR satellite, and the accuracy of FY-3/VIRR satellite data is described. There is a greater improvement, the ability to identify slightly worse than the MODIS satellite, the region is relatively large fire detection accuracy is higher.

With the industrialization and urbanization, the status of electricity in the production and life is getting higher and higher. So the prediction of power quality is the more potential significance. Traditional power quality analysis methods include: power quality data compression, disturbance event pattern classification, disturbance parameter calculation. Under certain conditions, these methods can predict power quality. This paper analyses the temporal variation of power quality of one provincial power grid in China from time angle. The distribution of power quality was analyzed based on spatial autocorrelation. This paper tries to prove that the research idea of geography is effective for mining the potential information of power quality.

High resolution image change detection is one of the key technologies of remote sensing application, which is of great significance for resource survey, environmental monitoring, fine agriculture, military mapping and battlefield environment detection. In this paper, for high-resolution satellite imagery, Random Forest (RF), Support Vector Machine (SVM), Deep belief network (DBN), and Adaboost models were established to verify the possibility of different machine learning applications in change detection. In order to compare detection accuracy of four machine learning Method, we applied these four machine learning methods for two high-resolution images. The results shows that SVM has higher overall accuracy at small samples compared to RF, Adaboost, and DBN for binary and from-to change detection. With the increase in the number of samples, RF has higher overall accuracy compared to Adaboost, SVM and DBN.

Object-based image analysis method has many advantages over pixel-based methods, so it is one of the current research hotspots. It is very important to get the image objects by multi-scale image segmentation in order to carry out object-based image analysis. The current popular image segmentation methods mainly share the bottom-up segmentation principle, which is simple to realize and the object boundaries obtained are accurate. However, the macro statistical characteristics of the image areas are difficult to be taken into account, and fragmented segmentation (or over-segmentation) results are difficult to avoid. In addition, when it comes to information extraction, target recognition and other applications, image targets are not equally important, i.e., some specific targets or target groups with particular features worth more attention than the others. To avoid the problem of over-segmentation and highlight the targets of interest, this paper proposes a multi-scale image segmentation method with visually saliency graph constraints. Visual saliency theory and the typical feature extraction method are adopted to obtain the visual saliency information, especially the macroscopic information to be analyzed. The visual saliency information is used as a distribution map of homogeneity weight, where each pixel is given a weight. This weight acts as one of the merging constraints in the multi- scale image segmentation. As a result, pixels that macroscopically belong to the same object but are locally different can be more likely assigned to one same object. In addition, due to the constraint of visual saliency model, the constraint ability over local-macroscopic characteristics can be well controlled during the segmentation process based on different objects. These controls will improve the completeness of visually saliency areas in the segmentation results while diluting the controlling effect for non- saliency background areas. Experiments show that this method works better for texture image segmentation than traditional multi-scale image segmentation methods, and can enable us to give priority control to the saliency objects of interest. This method has been used in image quality evaluation, scattered residential area extraction, sparse forest extraction and other applications to verify its validation. All applications showed good results.

Sea-land segmentation is one of the key technologies of sea target detection in remote sensing images. At present, the existing algorithms have the problems of low accuracy, low universality and poor automatic performance. This paper puts forward a sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image removing island. Firstly, the coastline data is extracted and all of land area is labeled by using the geographic information in large-field remote sensing image. Secondly, three features (local entropy, local texture and local gradient mean) is extracted in the sea-land border area, and the three features combine a 3D feature vector. And then the MultiGaussian model is adopted to describe 3D feature vectors of sea background in the edge of the coastline. Based on this multi-gaussian sea background model, the sea pixels and land pixels near coastline are classified more precise. Finally, the coarse segmentation result and the fine segmentation result are fused to obtain the accurate sea-land segmentation. Comparing and analyzing the experimental results by subjective vision, it shows that the proposed method has high segmentation accuracy, wide applicability and strong anti-disturbance ability.

For the sake of curbing the spreading of Changsha-Zhuzhou-Xiangtan urban agglomeration and spatial disorder in the process of urbanization development on the regional bearing capacity of land resources and ecological environment and assisting to plan the integration process of ChangZhuTan,this paper uses the DMSP/OLS night light data of Chang ZhuTan in 1992 to 2013 to invert the urbanization process index of ChangZhuTan urban agglomeration. Based on the two scales of time and space, this paper analyzes the average index of lights, the speed of urban expansion and urban compactness index et al and studies the temporal and spatial characteristics of ChangZhuTan urban agglomeration in this period.

Based on GIS and remote sensing technology, this paper estimates the NPP of the 2015 year-round and every season of Gansu province in northwest China by using the CASA(Carnegie Ames Stanford Approach) light energy utilization model. The result shows that the total annual NPP of Gansu province gradually decline from southeast to northwest in the space, which is in accordance with the water and heat condition in Gansu province. The results show that the summer NPP in Gansu Province is the maximum in each season. The maximum value of summer NPP in Gansu Province reached 695 (gCm-2•season-1), and the maximum value was 473 in spring, and 288 in the autumn, and the NPP in the winter in Gansu province were under 60. The fluctuation range of NPP value is large, this is due to the diversity of ecosystem types in Gansu province, including desert, grassland, farmland and forest, among them, the grassland area is the largest, and the grassland type is very diverse, the grassland coverage is obviously different, especially the low coverage grassland growth is affected by precipitation and temperature and other meteorological factors obviously.

In this article, we develop two methods for unsupervised change detection in multitemporal remote sensing images based on Dempster-Shafer's theory of evidence (DST). In most unsupervised change detection methods, the probability of difference image is assumed to be characterized by mixture models, whose parameters are estimated by the expectation maximization (EM) method. However, the main drawback of the EM method is that it does not consider spatial contextual information, which may entail rather noisy detection results with numerous spurious alarms. To remedy this, we firstly develop an evidence theory based EM method (EEM) which incorporates spatial contextual information in EM by iteratively fusing the belief assignments of neighboring pixels to the central pixel. Secondly, an evidential labeling method in the sense of maximizing a posteriori probability (MAP) is proposed in order to further enhance the detection result. It first uses the parameters estimated by EEM to initialize the class labels of a difference image. Then it iteratively fuses class conditional information and spatial contextual information, and updates labels and class parameters. Finally it converges to a fixed state which gives the detection result. A simulated image set and two real remote sensing data sets are used to evaluate the two evidential change detection methods. Experimental results show that the new evidential methods are comparable to other prevalent methods in terms of total error rate.

Fine particulate matter (aerodynamic diameters of less than 2.5 μm, PM2.5) air pollution has become one of the global environmental problem, endangering the existence of residents living, climate, and public health. Estimation Particulate Matter (aerodynamic diameters of less than 10 μm, PM10) concentration and aerosol absorption was the key point in air quality and climate studies. In this study, we retrieve the Aerosol Optical Depth (AOD) from the Earth Observing System (EOS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), and PM2.5, PM10 in winter on 2014 and 2015, using Extended Dense Dark Vegetation Algorithm and 6S radiation model to analysis the correlation. The result showed that at the condition of non-considering the influence of primary pollutants, the correlation of two Polynomials between aerosol optical depth and PM2.5 and PM10 was poor; taking the influence of the primary pollutants into consideration, the aerosol optical depth has a good correlation with PM2.5 and PM10. The version of PM10 by aerosol optical depth is higher than that of PM2.5, so the model can be used to realize the high precision inversion of winter PM10 in Lanzhou.

There has been increased interest over several decades for applying ground-based synthetic aperture radar (GB-SAR) for monitoring terrain displacement. GB-SAR can achieve multitemporal surface deformation maps of the entire terrain with high spatial resolution and submilimetric accuracy due to the ability of continuous monitoring a certain area day and night regardless of the weather condition. The accuracy of the interferometric measurement result is very important. In this paper, the basic principle of InSAR is expounded, the influence of the platform’s instability on the interferometric measurement results are analyzed. The error sources of deformation detection estimation are analyzed using precise geometry of imaging model. Finally, simulation results demonstrates the validity of our analysis.

Based on the Environmental Mitigation Satellite (HJ-1) data, this paper has carried on the remote sensing monitoring to change of the surrounding vegetation and water area of the Qingtu Lake since 2009. The result shows that the average area of water has increased by 3.59 square kilometres annually since the reappearance of the waters with the Qingtu Lake in 2010. The area of Qingtu Lake and surrounding vegetation cover has presented an average increase of 1.09 square kilometres per year. Since 2010, the precipitation of the Qingtu Lake and its surrounding area in Minqin county have a significant increase in the trend, the average increase rate of 6.0 mm/year. Compared to 2010 years ago, the average precipitation increased 36.4 mm. And it shows that the change of the Qingtu Lake underlying surface has a positive feedback effect to local heavy rainfall according to the comparative analysis of the precipitation observation in the surrounding weather station.

In this study, we compared four different water extraction methods with GF-1 data according to different water types in Tibet, including Support Vector Machine (SVM), Principal Component Analysis (PCA), Decision Tree Classifier based on False Normalized Difference Water Index (FNDWI-DTC), and PCA-SVM. The results show that all of the four methods can extract large area water body, but only SVM and PCA-SVM can obtain satisfying extraction results for small size water body. The methods were evaluated by both overall accuracy (OAA) and Kappa coefficient (KC). The OAA of PCA-SVM, SVM, FNDWI-DTC, PCA are 96.68%, 94.23%, 93.99%, 93.01%, and the KCs are 0.9308, 0.8995, 0.8962, 0.8842, respectively, in consistent with visual inspection. In summary, SVM is better for narrow rivers extraction and PCA-SVM is suitable for water extraction of various types. As for dark blue lakes, the methods using PCA can extract more quickly and accurately.

Remote sensing inversion of heavy metal in vegetation leaves is generally based on the physiological characteristics of vegetation spectrum under heavy metal stress, and empirical models with vegetation indices are established to inverse the heavy metal content of vegetation leaves. However, the research of inversion of heavy metal content in vegetation-covered soil is still rare. In this study, Pulang is chosen as study area. The regression model of a typical heavy metal element, copper (Cu), is established with vegetation indices. We mainly investigate the inversion accuracies of Cu element in vegetation-covered soil by different vegetation indices according to specific spectral resolutions of ASD (Analytical Spectral Device) and Hyperion data. The inversion results of soil copper content in the vegetation-covered area shows a good accuracy, and the vegetation indices under ASD spectral resolution correspond to better results.

There is an increasing interest in three-dimensional Synthetic Aperture Radar (3-D SAR) imaging from observed sparse scattering data. However, the existing 3-D sparse imaging method requires large computing times and storage capacity. In this paper, we propose a modified method for the sparse 3-D SAR imaging. The method processes the collection of noisy SAR measurements, usually collected over nonlinear flight paths, and outputs 3-D SAR imagery. Firstly, the 3-D sparse reconstruction problem is transformed into a series of 2-D slices reconstruction problem by range compression. Then the slices are reconstructed by the modified SL0 (smoothed l₀ norm) reconstruction algorithm. The improved algorithm uses hyperbolic tangent function instead of the Gaussian function to approximate the l₀ norm and uses the Newton direction instead of the steepest descent direction, which can speed up the convergence rate of the SL0 algorithm. Finally, numerical simulation results are given to demonstrate the effectiveness of the proposed algorithm. It is shown that our method, compared with existing 3-D sparse imaging method, performs better in reconstruction quality and the reconstruction time.

In this paper, an image classification algorithm for airport area is proposed, which based on the statistical features of synthetic aperture radar (SAR) images and the spatial information of pixels. The algorithm combines Gamma mixture model and MRF. The algorithm using Gamma mixture model to obtain the initial classification result. Pixel space correlation based on the classification results are optimized by the MRF technique. Additionally, morphology methods are employed to extract airport (ROI) region where the suspected aircraft target samples are clarified to reduce the false alarm and increase the detection performance. Finally, this paper presents the plane target detection, which have been verified by simulation test.

At present, due to the continuous expansion of city and the increase of the municipal drainage facilities, which leads to a serious lack of management and operation personnel, the existing production management pattern already can’t adapt to the new requirements. In this paper, according to river drainage management, flood control, water management, auditing, administrative license, etc. different business management requirement, an information management system for water planning and design of smart city based on WebGIS in Linyi was introduced, which can collect the various information of gate dam, water pump, bridge sensor and traffic guide terminal nodes etc. together. The practical application show that the system can not only implement the sharing, resources integration and collaborative application for the regional water information, but also improve the level of the integrated water management.

Electrical tape is found as one type of important trace evidence in crime scene. For example, it is very frequently used to insulate wires in explosive devices in many criminal cases. The fingerprints of the suspects were often left on the adhesive side of the tapes, which can provide very useful clues for the investigation and make it possible for individual identification. The most commonly used method to detect and visualize those latent fingerprints is to peel off each layer of the tapes first and then adopt the chemical methods to develop the fingerprints on the tapes. However, the peeling-off and chemical development process would degrade and contaminate the fingerprints and thus adversely affect the accuracy of identification. Optical coherence tomography (OCT) is a novel forensic imaging modality based on lowcoherence interferometry, which has the advantages of non-destruction, micrometer-level high resolution and crosssectional imaging. In this study, a fiber-based spectral-domain OCT (SD-OCT) system with ~6μm resolution was employed to obtain the image of fingerprint sandwiched between two opaque electrical tapes without any pre-processing procedure like peeling-off. Three-dimensional (3D) OCT reconstruction was performed and the subsurface image was produced to visualize the latent fingerprints. The results demonstrate that OCT is a promising tool for recovering the latent fingerprints hidden beneath opaque electrical tape non-destructively and rapidly.

Under the environment of system combat, in order to solve the problem on management and analysis of the massive heterogeneous data on multi-platform avionics system, this paper proposes a management solution which called avionics “resource cloud” based on big data technology, and designs an aided decision classifier based on SVM algorithm. We design an experiment with STK simulation, the result shows that this method has a high accuracy and a broad application prospect.

The study was to investigate the influence on stereoscopic cognition with different densities and dot sizes of static random-dot stereograms (RDS). The responses of every subject were recorded with different densities of RDS (10%, 20%, 30%, 40%) and different dot sizes of RDS (2*2 pix, 3*3 pix, 4*4 pix). The results showed that reaction times decreased with increasing densities of RDS and dot sizes of RDS. The reaction time was the shortest when the density of RDS was 30%. And when the dot size was 4*4 pix, the mean response time was the shortest. Therefore, when the density of RDS was 30% and the dot size of RDS was 4*4 pix, it was the most convenient for the stereoscopic cognition.

Image quality evaluation is a classic research topic, the goal is to design the algorithm, given the subjective feelings consistent with the evaluation value. This paper mainly introduces several typical reference methods of Mean Squared Error(MSE), Peak Signal to Noise Rate(PSNR), Structural Similarity Image Metric(SSIM) and feature similarity(FSIM) of objective evaluation methods. The different evaluation methods are tested by Matlab, and the advantages and disadvantages of these methods are obtained by analyzing and comparing them.MSE and PSNR are simple, but they are not considered to introduce HVS characteristics into image quality evaluation. The evaluation result is not ideal. SSIM has a good correlation and simple calculation ,because it is considered to the human visual effect into image quality evaluation,However the SSIM method is based on a hypothesis,The evaluation result is limited. The FSIM method can be used for test of gray image and color image test, and the result is better. Experimental results show that the new image quality evaluation algorithm based on FSIM is more accurate.

Multiwavelets have orthogonality, compacted support and symmetry simultaneously, these properties are very important for signal processing. However, most of Multiwavelets require related prefilters. An approach to construction of symmetry/antisymmetry orthogonal filter is proposed and its corresponding balanced filter is constructed, no any prefilter is necessary. Experimental results prove its performance is superior to DGHM and CL multiwavelets, higher than Bi9/7.

We propose a new frequency domain wavelet based watermarking technique. The key idea of our scheme is twofold: multi-tier solution representation of image and odd-even quantization embedding/extracting watermark. Because many complementary watermarks need to be hidden, the watermark image designed is image-adaptive. The meaningful and complementary watermark images was embedded into the original image (host image) by odd-even quantization modifying coefficients, which was selected from the detail wavelet coefficients of the original image, if their magnitudes are larger than their corresponding Just Noticeable Difference thresholds. The tests show good robustness against best-known attacks such as noise addition, image compression, median filtering, clipping as well as geometric transforms. Further research may improve the performance by refining JND thresholds.

Aiming at solving the problem of accurately positioning the detection probes underwater, this paper proposed a method based on computer vision which can effectively solve this problem. The theory of this method is that: First, because the shape information of the heat tube is similar to a circle in the image, we can find a circle which physical location is well known in the image, we set this circle as the reference circle. Second, we calculate the pixel offset between the reference circle and the probes in the picture, and adjust the steering gear through the offset. As a result, we can accurately measure the physical distance between the probes and the under test heat tubes, then we can know the precise location of the probes underwater. However, how to choose reference circle in image is a difficult problem. In this paper, we propose an algorithm that can adaptively confirm the area of reference circle. In this area, there will be only one circle, and the circle is the reference circle. The test results show that the accuracy of the algorithm of extracting the reference circle in the whole picture without using ROI (region of interest) of the reference circle is only 58.76% and the proposed algorithm is 95.88%. The experimental results indicate that the proposed algorithm can effectively improve the efficiency of the tubes detection.

As the gas leak infrared imaging detection technology has significant advantages of high efficiency and remote imaging detection, in order to enhance the detail perception of observers and equivalently improve the detection limit, we propose a new type of gas leak infrared image detection method, which combines background difference methods and multi-frame interval difference method. Compared to the traditional frame methods, the multi-frame interval difference method we proposed can extract a more complete target image. By fusing the background difference image and the multi-frame interval difference image, we can accumulate the information of infrared target image of the gas leak in many aspect. The experiment demonstrate that the completeness of the gas leakage trace information is enhanced significantly, and the real-time detection effect can be achieved.

In order to detect the invisible leaking gas that is usually dangerous and easily leads to fire or explosion in time, many new technologies have arisen in the recent years, among which the infrared video based gas leak detection is widely recognized as a viable tool. However, all the moving regions of a video frame can be detected as leaking gas regions by the existing infrared video based gas leak detection methods, without discriminating the property of each detected region, e.g., a walking person in a video frame may be also detected as gas by the current gas leak detection methods.To solve this problem, we propose a novel infrared video based gas leak detection method in this paper, which is able to effectively suppress strong motion disturbances.Firstly, the Gaussian mixture model(GMM) is used to establish the background model.Then due to the observation that the shapes of gas regions are different from most rigid moving objects, we modify the Features From Accelerated Segment Test (FAST) algorithm and use the modified FAST (mFAST) features to describe each connected component. In view of the fact that the statistical property of the mFAST features extracted from gas regions is different from that of other motion regions, we propose the Pixel-Per-Points (PPP) condition to further select candidate connected components.Experimental results show that the algorithm is able to effectively suppress most strong motion disturbances and achieve real-time leaking gas detection.

Background modeling plays an important role in the task of gas detection based on infrared video. VIBE algorithm is a widely used background modeling algorithm in recent years. However, the processing speed of the VIBE algorithm sometimes cannot meet the requirements of some real time detection applications. Therefore, based on the traditional VIBE algorithm, we propose a fast prospect model and optimize the results by combining the connected domain algorithm and the nine-spaces algorithm in the following processing steps. Experiments show the effectiveness of the proposed method.

The colorimetric characterization of the display can achieve the purpose of precisely controlling the color of the monitor. This paper describes an improved method for estimating the gamma value of liquid-crystal displays (LCDs) without using a measurement device was described by Xiao et al. It relies on observer’s luminance matching by presenting eight half-tone patterns with luminance from 1/9 to 8/9 of the maximum value of each color channel. Since the previous method lacked partial low frequency information, we partially replaced the half-tone patterns. A large number of experiments show that the color difference is reduced from 3.726 to 2.835, and our half-tone pattern can better estimate the visual gamma value of LCDs.

At present, content-based video retrieval (CBVR) is the most mainstream video retrieval method, using the video features of its own to perform automatic identification and retrieval. This method involves a key technology, i.e. shot segmentation. In this paper, the method of automatic video shot boundary detection with K-means clustering and improved adaptive dual threshold comparison is proposed. First, extract the visual features of every frame and divide them into two categories using K-means clustering algorithm, namely, one with significant change and one with no significant change. Then, as to the classification results, utilize the improved adaptive dual threshold comparison method to determine the abrupt as well as gradual shot boundaries.Finally, achieve automatic video shot boundary detection system.

Black point is the point at which RGB's single channel digital drive value is 0. Due to the problem of light leakage of liquid-crystal displays (LCDs), black point’s luminance value is not 0, this phenomenon bring some errors to colorimetric characterization of LCDs, especially low luminance value driving greater sampling effect. This paper describes the characteristic accuracy of polynomial model method and the effect of black point on accuracy, the color difference accuracy is given. When considering the black point in the characteristics equation, the maximum color difference is 3.246, the maximum color difference than without considering the black points reduced by 2.36. The experimental results show that the accuracy of LCDs colorimetric characterization can be improved, if the effect of black point is eliminated properly.

The goal of steganography is to embed secret data into a cover so no one apart from the sender and intended recipients can find the secret data. Usually, the way the cover changing was decided by a hidden function. There were no existing model could be used to find an optimal function which can greatly reduce the distortion the cover suffered. This paper considers the cover carrying secret message as a random Markov chain, taking the advantages of a deterministic relation between initial distributions and transferring matrix of the Markov chain, and takes the transferring matrix as a constriction to decrease statistical distortion the cover suffered in the process of information hiding. Furthermore, a hidden function is designed and the transferring matrix is also presented to be a matrix from the original cover to the stego cover. Experiment results show that the new model preserves a consistent statistical characterizations of original and stego cover.

The current vehicle key is easy to be destroyed and damage, proposing the use of elliptical encryption algorithm is improving the reliability of vehicle security system. Based on the encryption rules of elliptic curve, the chip’s framework and hardware structure are designed, then the chip calculation process simulation has been analyzed by software. The simulation has been achieved the expected target. Finally, some issues pointed out in the data calculation about the chip's storage、control and other modules.

The rapid development of avionics systems is driving the application of integrated modular avionics (IMA) systems. But meanwhile it is improving avionics system integration, complexity of system test. Then we need simplify the method of IMA system test. The IMA system supports a module platform that runs multiple applications, and shares processing resources. Compared with federated avionics system, IMA system is difficult to isolate failure. Therefore, IMA system verification will face the critical problem is how to test shared resources of multiple application. For a simple avionics system, traditional test methods are easily realizing to test a whole system. But for a complex system, it is hard completed to totally test a huge and integrated avionics system. Then this paper provides using compositional-verification theory in IMA system test, so that reducing processes of test and improving efficiency, consequently economizing costs of IMA system integration.

The availability of dense time series of Landsat images pro-vides a great chance to reconstruct forest disturbance and change history with high temporal resolution, medium spatial resolution and long period. This proposal aims to apply forest change detection method in Hainan Jianfengling Forest Park using yearly Landsat time-series images. A simple detection method from the dense time series Landsat NDVI images will be used to reconstruct forest change history (afforestation and deforestation). The mapping result showed a large decrease occurred in the extent of closed forest from 1980s to 1990s. From the beginning of the 21st century, we found an increase in forest areas with the implementation of forestry measures such as the prohibition of cutting and sealing in our study area. Our findings provide an effective approach for quickly detecting forest changes in tropical original forest, especially for afforestation and deforestation, and a comprehensive analysis tool for forest resource protection.

Remote sensing has an important means of water depth detection in coastal shallow waters and reefs. Support vector regression (SVR) is a machine learning method which is widely used in data regression. In this paper, SVR is used to remote sensing multispectral bathymetry. Aiming at the problem that the single-kernel SVR method has a large error in shallow water depth inversion, the mean relative error (MRE) of different water depth is retrieved as a decision fusion factor with single kernel SVR method, a multi kernel SVR fusion method based on the MRE is put forward. And taking the North Island of the Xisha Islands in China as an experimentation area, the comparison experiments with the single kernel SVR method and the traditional multi-bands bathymetric method are carried out.

The results show that: 1) In range of 0 to 25 meters, the mean absolute error(MAE)of the multi kernel SVR fusion method is 1.5m,the MRE is 13.2%; 2) Compared to the 4 single kernel SVR method, the MRE of the fusion method reduced 1.2%、1.9%、3.4%、1.8%, and compared to traditional multi-bands method, the MRE reduced 1.9%; 3) In 0-5m depth section, compared to the single kernel method and the multi-bands method, the MRE of fusion method reduced 13.5% to 44.4%, and the distribution of points is more concentrated relative to y=x.

Based on STC89C52 chip, this paper presents a single chip microcomputer minimum system, which is used to realize the logic control of digital speech storage and playback system. Compared with the traditional tape voice recording system, the system has advantages of small size, low power consumption, The effective solution of traditional voice recording system is limited in the use of electronic and information processing.

Based on the idea of iteration, a simple and effective method for multi-focus image fusion is proposed. Firstly, two images are processed by Gaussian convolution, and the blurred images are obtained. Then, the area decision of image blocks are obtained through the original image subtracting the blurred images at different blocks and different levels. According to the principle of consistency, the subjection of image blocks is obtained by the area decision of image blocks. The area partition map is obtained by the subjection map at different levels. Finally, the fused image is obtained through the method of coefficient weighting. The experimental results show that the image quality obtained by the proposed method is superior to the traditional fusion method.

Obstacle detection is of essential importance for Unmanned Surface Vehicles (USV). Although some obstacles (e.g., ships, islands) can be detected by Radar, there are many other obstacles (e.g., floating pieces of woods, swimmers) which are difficult to be detected via Radar because these obstacles have low radar cross section. Therefore, detecting obstacle from images taken onboard is an effective supplement. In this paper, a robust vision-based obstacle detection method for USVs is developed. The proposed method employs the monocular image sequence captured by the camera on the USVs and detects obstacles on the sea surface from the image sequence. The experiment results show that the proposed scheme is efficient to fulfill the obstacle detection task.

Chlorophyll is an important pigment in green plants for photosynthesis and obtaining the energy for growth and development. The rapid, nondestructive and accurate estimation of chlorophyll content is significant for understanding the crops growth, monitoring the disease and insect, and assessing the yield of crops. Sentinel-2 equipped with the Multi-Spectral Instrument (MSI), which will provide images with high spatial, spectral and temporal resolution. It covers the VNIR/SWIR spectral region in 13 bands and incorporates two new spectral bands in the red-edge region and a spatial resolution of 20nm, which can be used to derive vegetation indices using red-edge bands. In this paper, we will focus on assessing the potential of vegetation spectral indices for retrieving chlorophyll content from Sentinel-2 at different angles. Subsequently, we used in-situ spectral data and Sentinel-2 data to test the relationship between VIs and chlorophyll content. The REP, MTCI, CI_red-edge, CI_green, Macc01, TCARI/OSAVI [705,750], NDRE1 and NDRE2 were calculated. NDRE2 index displays a strongly similar result for hyperspectral and simulated Sentinel-2 spectral bands (R² =0.53, R² =0.51, for hyperspectral and Sentinel-2, respectively). At different observation angles, NDRE2 has the smallest difference in performance (R² = 0.51, R² =0.64, at 0° and 15° , respectively).

Due to the complex constraints, more uncertain factors and critical real-time demand of path planning for multiple unmanned surface vehicle (multi-USV), an improved artificial bee colony (I-ABC) algorithm were proposed to solve the model of cooperative path planning for multi-USV. First the Voronoi diagram of battle field space is conceived to generate the optimal area of USVs paths. Then the chaotic searching algorithm is used to initialize the collection of paths, which is regard as foods of the ABC algorithm. With the limited data, the initial collection can search the optimal area of paths perfectly. Finally simulations of the multi-USV path planning under various threats have been carried out. Simulation results verify that the I-ABC algorithm can improve the diversity of nectar source and the convergence rate of algorithm. It can increase the adaptability of dynamic battlefield and unexpected threats for USV.

A signal conditioning units for vibration measurement in HUMS is proposed in the paper. Due to the frequency of vibrations caused by components in helicopter are different, two steps amplifier and programmable anti-aliasing filter are designed to meet the measurement of different types of helicopter. Vibration signals are converted into measurable electrical signals combing with ICP driver firstly. Then pre-amplifier and programmable gain amplifier is applied to magnify the weak electrical signals. In addition, programmable anti-aliasing filter is utilized to filter the interference of noise. The units were tested using function signal generator and oscilloscope. The experimental results have demonstrated the effectiveness of our proposed method in quantitatively and qualitatively. The method presented in this paper can meet the measurement requirement for different types of helicopter.

By using optical image processing techniques, a novel text encryption and hiding method applied by double-random phase-encoding technique is proposed in the paper. The first step is that the secret message is transformed into a 2-dimension array. The higher bits of the elements in the array are used to fill with the bit stream of the secret text, while the lower bits are stored specific values. Then, the transformed array is encoded by double random phase encoding technique. Last, the encoded array is embedded on a public host image to obtain the image embedded with hidden text. The performance of the proposed technique is tested via analytical modeling and test data stream. Experimental results show that the secret text can be recovered either accurately or almost accurately, while maintaining the quality of the host image embedded with hidden data by properly selecting the method of transforming the secret text into an array and the superimposition coefficient.

After a deep analysis on how to use an image processing system to detect the missing holes on the motor carling, we design the whole system combined with the actual production conditions of the motor carling. Afterwards we explain the whole system’s hardware and software in detail. We introduce the general functions for the system’s hardware and software. Analyzed these general functions, we discuss the modules of the system’s hardware and software and the theory to design these modules in detail. The measurement to confirm the area to image processing, edge detection, randomized Hough transform to circle detecting is explained in detail. Finally, the system result tested in the laboratory and in the factory is given out.

With the rapid development of Urban Rail Transit Signal Technology, the demand of signal power supply system for signal equipment is higher and higher. The signal intelligent power supply panel is the main component of the urban rail traffic signal power supply system. Whether the intelligent power supply panel working or not is directly related to traffic safety. The maintenance of intelligent signal power supply panel is particularly important. Line 7 of Shanghai Metro adopts PMZG Signal Intelligent Power Supply Panel, which is produced by Beijing Jinyujiaxin Polytron Technologies Inc. Maintenance of power supply system mainly includes routine maintenance and troubleshooting. This article will make clear the routine maintenance contents of PMZG Signal Intelligent Power Supply Panel, and put forward the common fault information and troubleshooting methods of PMZG Signal Intelligent Power Supply Panel. In accordance with the steps of fault handling, the faults can be eliminated in the shortest possible time, and PMZG Signal Intelligent Power Supply Panel can be quickly restored to normal working state.

Focused on the issue that the decrease of convergence speed and the precision of calculation at the end of the process in Artificial Fish Swarm Algorithm(AFSA) and instability of results, a hybrid AFSA based on similar fragments is proposed. Traditional AFSA enjoys a lot of obvious advantages in solving complex optimization problems like Vehicle Routing Problem(VRP). AFSA have a few limitations such as low convergence speed, low precision and instability of results. In this paper, two improvements are introduced. On the one hand, change the definition of the distance for artificial fish, as well as increase vision field of artificial fish, and the problem of speed and precision can be improved when solving VRP. On the other hand, mix artificial bee colony algorithm(ABC) into AFSA - initialize the population of artificial fish by the ABC, and it solves the problem of instability of results in some extend. The experiment results demonstrate that the optimal solution of the hybrid AFSA is easier to approach the optimal solution of the standard database than the other two algorithms. In conclusion, the hybrid algorithm can effectively solve the problem that instability of results and decrease of convergence speed and the precision of calculation at the end of the process.

It is a great challenging task to complete an automatic search of objects underwater. Usually the forward looking sonar is used to find the target, and then the initial identification of the target is completed by the side-scan sonar, and finally the confirmation of the target is accomplished by underwater TV. This paper presents an efficient method for automatic extraction of man-made sensitive targets in underwater TV. Firstly, the image of underwater TV is simplified with taking full advantage of the prior knowledge of the target and the background; then template matching technology is used for target detection; finally the target is confirmed by extracting parallel lines on the target contour. The algorithm is formulated for real-time execution on limited-memory commercial-of-the-shelf platforms and is capable of detection objects in underwater TV.

Automatic facial expression recognition has been one of the research hotspots in the area of computer vision for nearly ten years. During the decade, many state-of-the-art methods have been proposed which perform very high accurate rate based on the face images without any interference. Nowadays, many researchers begin to challenge the task of classifying the facial expression images with corruptions and occlusions and the Sparse Representation based Classification framework has been wildly used because it can robust to the corruptions and occlusions. Therefore, this paper proposed a novel facial expression recognition method based on Weber local descriptor (WLD) and Sparse representation. The method includes three parts: firstly the face images are divided into many local patches, and then the WLD histograms of each patch are extracted, finally all the WLD histograms features are composed into a vector and combined with SRC to classify the facial expressions. The experiment results on the Cohn-Kanade database show that the proposed method is robust to occlusions and corruptions.

In this paper, we proposed an image-based approach for automatic recognizing the flowering stage of maize. A modified HOG/SVM detection framework is first adopted to detect the ears of maize. Then, we use low-rank matrix recovery technology to precisely extract the ears at pixel level. At last, a new feature called color gradient histogram, as an indicator, is proposed to determine the flowering stage. Comparing experiment has been carried out to testify the validity of our method and the results indicate that our method can meet the demand for practical observation.

Flood is one of the major disasters in China. There are heavy intensity and wide range rainstorm during flood season in eastern part of China, and the flood control capacity of rivers is lower somewhere, so the flood disaster is abrupt and caused lots of direct economic losses. In this paper, based on BJ-2 Spatio-temporal resolution remote sensing data, reference image, 30-meter Global Land Cover Dataset(GlobeLand 30) and basic geographic data, forming Dam break monitoring model which including BJ-2 date processing sub-model, flood inundation range monitoring sub-model, dam break change monitoring sub-model and crop inundation monitoring sub-model. Case analysis in Poyang County Jiangxi province in 20^th, Jun, 2016 show that the model has a high precision and could monitoring flood inundation range, crops inundation range and breach.

Now, Terahertz technology has been widely used in many fields, which is mainly related to imaging detection. While the frequency range of the terahertz-wave is located between microwave and visible light, whether the existing visible light principle is applicable to terahertz-wave should be studied again. Through experiment, we measure the terahertz-wave field amplitude distribution on the receiving plane perpendicular to the direction of propagation in the air and picture out the energy distribution curve; derive an energy decay formula of terahertz wave based on the results; design a terahertz wavelength apparatus using the F-P interferometer theory; test the wavelength between 1 and 3 THz from the SIFIR-50THz laser of American Corehent company; finally analyze the related factors affecting the measurement precision including the beam incident angle, mechanical vibration, temperature fluctuation and the refractive index fluctuation.